Authors' objectives: Attention deficit hyperactivity disorder is characterised by inattention, impulsivity and hyperactivity. Diagnosis is complex and time-consuming. Medication requires careful selection and dose titration. Technologies for objective measures of attention deficit hyperactivity disorder that use motion sensors to measure hyperactivity (‘sensor continuous performance tests’) may help improve the diagnostic process and medication management when used in addition to clinical assessment. To determine whether sensor continuous performance tests are clinically effective and cost-effective to the National Health Service. Specific objectives were to determine the effectiveness of sensor continuous performance tests for: diagnosis of attention deficit hyperactivity disorder in people referred with suspected attention deficit hyperactivity disorder diagnosis of attention deficit hyperactivity disorder in people referred with suspected attention deficit hyperactivity disorder for whom current assessment cannot reach a diagnosis during initial dose titration and treatment decisions for people with attention deficit hyperactivity disorder evaluating treatment effectiveness during long-term treatment monitoring for people with attention deficit hyperactivity disorder. Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that is characterised by persistent patterns of inattention, impulsivity and hyperactivity that can significantly impact daily functioning. Diagnosis of ADHD is complex and relies on a clinician’s judgement combined with information such as questionnaires, third-party reports, patient history and behavioural observations. ADHD is frequently associated with other neurodevelopmental and psychiatric conditions, which can complicate the diagnosis and management of ADHD. It usually takes an average of two to three appointments and around 2.5 hours of clinic time to reach a diagnosis of ADHD. NHS waiting times for ADHD assessment are long, with patients often waiting more than 2 years. One treatment option for ADHD is medication. Identifying the most suitable medication and dose for a particular patient can be challenging. A number of rating scales and tests are available to help diagnose ADHD, but none have sufficient accuracy to be used as a stand-alone diagnostic tool. There are a number of technologies for objective measures of ADHD, which use motion sensors to measure hyperactivity [referred to as ‘sensor continuous performance test (CPT)’]. These may help to improve the diagnostic process for people with ADHD and to improve medication management when used in addition to standard clinical assessment. The overall aim of this project was to determine whether sensor CPTs are clinically effective and cost-effective to the NHS. Objective 1: What are the diagnostic accuracy and clinical effectiveness and cost-effectiveness of sensor CPT for the diagnosis of ADHD in people referred with suspected ADHD? Objective 2: What are the diagnostic accuracy and clinical effectiveness and cost-effectiveness of sensor CPT for the diagnosis of ADHD in people referred with suspected ADHD for whom current assessment cannot reach a diagnosis? Objective 3: What are the clinical effectiveness and cost-effectiveness of sensor CPT in evaluating medication effectiveness during initial dose titration and treatment decisions for people with a diagnosis of ADHD? Objective 4: What are the clinical effectiveness and cost-effectiveness of sensor-based CPT for evaluating treatment effectiveness during long-term treatment monitoring for people with a diagnosis of ADHD? We included 29 studies (38 reports) for objective 1: 2 RCTs (1 of these also provided data on accuracy; both included a survey and qualitative substudy); 20 DTA studies (2 included a survey of patient views); 5 uncontrolled before–after implementation studies (2 also provided information on patient/clinician views – 1 survey and qualitative evaluation, 1 survey) and 2 studies that only reported on patient’s and clinician’s acceptability of sensor CPTs. Most studies evaluated the QbTest, two evaluated EF Sim and two evaluated Nesplora Kids; there were no studies of EF Sim web or of Nesplora Adults. The majority of the evidence was in children. Five studies evaluated the accuracy of the QbTest in combination with clinical information; only one of these (the AQUA trial) evaluated the accuracy in combination with clinical judgement, as would be used in practice. However, data from the AQUA trial were limited due to inclusion of only those who had a diagnostic decision at 6 months and limitations with the reference standard. There are therefore no reliable data on the accuracy of any of the sensor CPTs when used in combination with clinical judgement. Estimates of the accuracy of the sensor CPTs alone were heterogeneous, and so results should be interpreted with caution. Summary estimates of the accuracy of the QbTest suggested that the sensitivity was highest when the subcomponents were combined into an overall measure (summary sensitivity 79%, 95% CI 69% to 86%), but specificity was lower (summary specificity 59%, 95% CI 42% to 74%) than when the subcategories were assessed individually. There was little evidence of a difference between the accuracy of the three subcategories of activity, impulsivity and inattention. One study of Nesplora Kids and two studies of EF Sim reported similar estimates of accuracy to studies of the QbTest, but this was based on very limited information from studies at a high risk of bias. Three studies provided a direct comparison between sensor CPT and non-sensor CPT, one study (the AQUA trial) provided a direct comparison between clinical diagnosis combined with QbTest with the accuracy of clinical diagnosis alone and one compared the accuracy of the QbTest alone to the accuracy of QbTest plus clinical information. One study reported that an overall measure from EF Sim was more sensitive than the non-sensor CPT omission errors measure (p = 0.03) but was less specific (p = 0.07). There was no difference between the overall EF Sim measure and the other two CPT measures. Two studies provided a direct comparison between the Conners’ CPT II and the QbTest (12–60). One reported that Qb measures were more sensitive (p ≤ 0.01) but less specific than the two Conners’ CPT measures, while the other reported that the QbTest was less sensitive (p 

Authors' results and conclusions: Objective 1 [29 studies – 25 QbTest (QbTech Ltd., Stockholm, Sweden), 2 EF Sim (Peili Vision, Oulu, Finland) and 2 Nesplora Kids (Giunti Psychometrics, Florence, Italy)]: most evidence was in children. The AQUA trial was the only study to evaluate the QbTest in combination with clinical assessment and included a comparison with clinical assessment alone. Accuracy was similar and there was no statistical evidence of a difference between groups (p = 0.14), but the study was at high risk of bias. The AQUA trial reported that adding QbTest to the diagnostic process resulted in fewer appointments to reach a diagnosis, reduced consultation time, greater clinician confidence and exclusion of the diagnosis in a more children. Findings were supported by limited data from uncontrolled before–after studies. Qualitative and survey data reported increased clinician confidence in clinical decision-making, reduced time to diagnostic decision and improved communication. Barriers to implementation included staffing, training, technology requirements and length and repetitive content of the test. We found that using QbTest in addition to clinical assessment was likely cost-effective due to the reduced time waiting for assessment, reduced appointments until diagnosis and a higher proportion receiving treatment benefits. Objective 3 (six studies): All evaluated QbTest and most had concerns with risk of bias. Qualitative and survey data suggested that healthcare staff and families valued the QbTest for dose titration, checking medication utility and improving medication adherence. Some data suggested that results may not increase patient understanding and some clinicians highlighted logistical challenges. No studies were identified for objectives 2 and 4. Our results suggest that QbTesting as part of the diagnostic workup for attention deficit hyperactivity disorder in children (age 

Authors' methods: Systematic review and economic model (searches completed 17 November 2023). Lack of good-quality data on all tests, both for diagnosis and medication management, particularly when evaluated in combination with clinical information. Clinical effectiveness review A systematic review was conducted. Studies that evaluated the QbMini (QbTech Ltd., Stockholm, Sweden), QbTest (6–12 and 12–60) (QbTech Ltd., Stockholm, Sweden), QbCheck (QbTech Ltd., Stockholm, Sweden), EF Sim (Peili Vision, Oulu, Finland), EF Sim Web Version (Peili Vision, Oulu, Finland), Nesplora Kids (Giunti Psychometrics, Florence, Italy) and Nesplora Adults (Giunti Psychometrics, Florence, Italy), alone or in combination with clinical assessment for ADHD, were eligible for inclusion. We included randomised controlled trials (RCTs), non-randomised studies of interventions, including before–after studies [non-randomised study of interventions (NRSI)], diagnostic test accuracy (DTA) studies, surveys and qualitative evaluations that reported on eligible outcomes. Four databases and two trial registries were searched (inception – 17 November 2023). We screened trial registries, reference lists of reviews and study reports, relevant websites and information submitted by test manufacturers. Title and abstract screening were conducted by two reviewers independently. Inclusion assessment, data extraction and risk-of-bias assessment were performed by one reviewer and checked by a second. Risk of bias was assessed using the following tools: Cochrane Risk of Bias Tool (RCTs), Risk Of Bias In Non-randomized Studies – of Interventions, QUADAS-2 (DTA studies), Critical Appraisal Skills Programme checklist (qualitative studies), Quality Assessment Checklist for Survey Studies in Psychology (survey studies). For each objective, we provided a narrative summary of study details, risk of bias and results. Random and fixed-effects meta-analyses were performed to generate summary effect estimates. Forest plots were produced to show individual and summary effect estimates with 95% confidence intervals (CIs). Fisher’s exact test was used to compare the estimates of accuracy where studies evaluated multiple index tests. Qualitative evidence was synthesised based on guidance from Joanna Briggs Institute.

Project Status: Completed

URL for project: https://www.journalslibrary.nihr.ac.uk/programmes/hta/NIHR136009

Year Published: 2025

URL for published report: https://www.journalslibrary.nihr.ac.uk/hta/DRDR7171

URL for additional information: English

English language abstract: An English language summary is available

Publication Type: Full HTA

Country: England, United Kingdom

DOI: 10.3310/DRDR7171

Organisation Name: NIHR Health Technology Assessment programme

Contact Address: NIHR Journals Library, National Institute for Health and Care Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK

Contact Name: journals.library@nihr.ac.uk

Contact Email: journals.library@nihr.ac.uk